Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification

Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is use...

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Autores principales:	Sundar Santhoshkumar, Vijayakumar Varadarajan, S. Gavaskar, J. Jegathesh Amalraj, A. Sumathi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multilevel thresholding DenseNet deep learning ICH diagnosis CT images computer-aided diagnosis Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/c79d36c2e78f4e169afb14b30769c6c0
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spelling	oai:doaj.org-article:c79d36c2e78f4e169afb14b30769c6c02021-11-11T15:36:47ZMachine Learning Model for Intracranial Hemorrhage Diagnosis and Classification10.3390/electronics102125742079-9292https://doaj.org/article/c79d36c2e78f4e169afb14b30769c6c02021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2574https://doaj.org/toc/2079-9292Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%.Sundar SanthoshkumarVijayakumar VaradarajanS. GavaskarJ. Jegathesh AmalrajA. SumathiMDPI AGarticlemultilevel thresholdingDenseNetdeep learningICH diagnosisCT imagescomputer-aided diagnosisElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2574, p 2574 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multilevel thresholding DenseNet deep learning ICH diagnosis CT images computer-aided diagnosis Electronics TK7800-8360
spellingShingle	multilevel thresholding DenseNet deep learning ICH diagnosis CT images computer-aided diagnosis Electronics TK7800-8360 Sundar Santhoshkumar Vijayakumar Varadarajan S. Gavaskar J. Jegathesh Amalraj A. Sumathi Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
description	Intracranial hemorrhage (ICH) is a pathological disorder that necessitates quick diagnosis and decision making. Computed tomography (CT) is a precise and highly reliable diagnosis model to detect hemorrhages. Automated detection of ICH from CT scans with a computer-aided diagnosis (CAD) model is useful to detect and classify the different grades of ICH. Because of the latest advancement of deep learning (DL) models on image processing applications, several medical imaging techniques utilize it. This study develops a new densely connected convolutional network (DenseNet) with extreme learning machine (ELM)) for ICH diagnosis and classification, called DN-ELM. The presented DL-ELM model utilizes Tsallis entropy with a grasshopper optimization algorithm (GOA), named TEGOA, for image segmentation and DenseNet for feature extraction. Finally, an extreme learning machine (ELM) is exploited for image classification purposes. To examine the effective classification outcome of the proposed method, a wide range of experiments were performed, and the results are determined using several performance measures. The simulation results ensured that the DL-ELM model has reached a proficient diagnostic performance with the maximum accuracy of 96.34%.
format	article
author	Sundar Santhoshkumar Vijayakumar Varadarajan S. Gavaskar J. Jegathesh Amalraj A. Sumathi
author_facet	Sundar Santhoshkumar Vijayakumar Varadarajan S. Gavaskar J. Jegathesh Amalraj A. Sumathi
author_sort	Sundar Santhoshkumar
title	Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
title_short	Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
title_full	Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
title_fullStr	Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
title_full_unstemmed	Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification
title_sort	machine learning model for intracranial hemorrhage diagnosis and classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c79d36c2e78f4e169afb14b30769c6c0
work_keys_str_mv	AT sundarsanthoshkumar machinelearningmodelforintracranialhemorrhagediagnosisandclassification AT vijayakumarvaradarajan machinelearningmodelforintracranialhemorrhagediagnosisandclassification AT sgavaskar machinelearningmodelforintracranialhemorrhagediagnosisandclassification AT jjegatheshamalraj machinelearningmodelforintracranialhemorrhagediagnosisandclassification AT asumathi machinelearningmodelforintracranialhemorrhagediagnosisandclassification
_version_	1718435013163220992

Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification

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